Predicting soil organic carbon with ensemble learning techniques by using satellite images for precision farming
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View abstract on PubMed
Summary
This summary is machine-generated.This study introduces a machine learning system to assess soil organic carbon using satellite data and soil properties. The XGBoost model achieved high accuracy, aiding farmers in precise fertilizer application for improved crop yields.
Area Of Science
- Agricultural Science
- Environmental Science
- Data Science
Background
- Soil composition is vital for agriculture, with soil organic carbon (SOC) being central to soil health and sustainable practices.
- Accurate SOC assessment enables informed farming decisions, optimizing crop growth and resource management.
Purpose Of The Study
- To develop a machine learning (ML) system for evaluating soil organic carbon (SOC) using topographic, remote sensing, and climate data.
- To compare the performance of different ML algorithms, including XGBoost, Random Forest, and Stacking, for SOC prediction.
Main Methods
- Utilized satellite-derived topographic variables, remote sensing indices, and climate data as predictor variables.
- Employed soil health card data as the dependent variable for model training and validation.
- Applied and evaluated XGBoost, Random Forest, and Stacking ensemble methods for SOC estimation.
Main Results
- XGBoost demonstrated superior performance with the highest R-squared (0.95) and lowest RMSE (0.03) during training.
- For the testing dataset, XGBoost and Random Forest showed varying performance metrics, with Stacking effectively mitigating overfitting.
- The developed system aims to provide precise fertilizer recommendations, thereby enhancing crop yield.
Conclusions
- Machine learning techniques applied to remote sensing data offer a robust approach for building decision support systems in precision agriculture.
- The proposed system can significantly assist farmers in optimizing agricultural inputs and improving overall farm productivity.
- Accurate SOC monitoring through ML is crucial for advancing sustainable agriculture and ensuring food security.
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